add a new parameter fix_data (#103)

angle_emb/angle.py

@@ -393,6 +393,7 @@ class AngleDataTokenizer:
     :param dataset_format: Optional[str]. Specify dataset_format from DatasetFormats. Default None.
         It will automatically detect the dataset format.
     :param end_with_eos: bool. Specify whether ends with the eos token. Default False.
+    :param fix_data: bool. Specify whether fix the data. Only works when prompt_template is not None. Default True.
 
     Example::
 
@@ -415,14 +416,16 @@ def __init__(self,
                  template_placeholders: Optional[List[str]] = None,
                  extra_columns: Optional[List[str]] = None,
                  dataset_format: Optional[str] = None,
-                 end_with_eos: bool = False):
+                 end_with_eos: bool = False,
+                 fix_data: bool = True):
         self.tokenizer = tokenizer
         self.max_length = max_length
         self.prompt_template = prompt_template
         self.prompt_template_tok = None
         self.extra_columns = extra_columns
         self.dataset_format = dataset_format
         self.end_with_eos = end_with_eos
+        self.fix_data = fix_data
         if template_placeholders is None:
             template_placeholders = ['condition', 'text']
         if prompt_template is not None:
@@ -492,7 +495,7 @@ def __call__(self, data: Dict) -> Dict:
         for text_column in text_columns:
             toks.append(self.tokenizer(data[text_column], max_length=self.max_length, truncation=True))
 
-        if self.prompt_template_tok is not None:
+        if self.prompt_template_tok is not None and self.fix_data:
             for tok in toks:
                 if tok['input_ids'][-1] != self.prompt_template_tok['input_ids'][-1]:
                     logger.info(f"data data: token ids={tok['input_ids']}, prompt_token_ids={self.prompt_template_tok['input_ids']}")  # NOQA

angle_emb/angle_trainer.py

@@ -47,6 +47,8 @@
                          'This prompt will be applied for all text columns.'
                          'If you want to specify different prompts for different text columns,'
                          'please handle it in the preprocessing step.')
+parser.add_argument('--fix_data', type=int, default=1, choices=[0, 1],
+                    help='Whether fix data (only works when prompt_template is not None), choices [0, 1], defaut 1')
 parser.add_argument('--filter_duplicate', type=int, default=1, choices=[0, 1],
                     help='Specify filter_duplicate, choices [0, 1], defaut 1')
 parser.add_argument('--save_dir', type=str, default=None,
@@ -221,11 +223,15 @@ def main():
     logger.info('Processing train...')
     if args.streaming:
         train_ds = ds[args.train_split_name].shuffle(args.dataset_seed).map(
-            AngleDataTokenizer(model.tokenizer, model.max_length, prompt_template=args.prompt_template),
+            AngleDataTokenizer(model.tokenizer, model.max_length,
+                               prompt_template=args.prompt_template,
+                               fix_data=args.fix_data),
             num_proc=args.workers)
     else:
         train_ds = ds[args.train_split_name].shuffle(args.dataset_seed).map(
-            AngleDataTokenizer(model.tokenizer, model.max_length, prompt_template=args.prompt_template),
+            AngleDataTokenizer(model.tokenizer, model.max_length,
+                               prompt_template=args.prompt_template,
+                               fix_data=args.fix_data),
             num_proc=args.workers)
 
     valid_ds = None
@@ -239,7 +245,9 @@ def main():
             else:
                 valid_ds = load_dataset(args.valid_name_or_path, num_proc=args.workers)
         valid_ds = valid_ds[args.valid_split_name or 'train'].map(
-            AngleDataTokenizer(model.tokenizer, model.max_length, prompt_template=args.prompt_template),
+            AngleDataTokenizer(model.tokenizer, model.max_length,
+                               prompt_template=args.prompt_template,
+                               fix_data=args.fix_data),
             num_proc=args.workers)
 
     valid_ds_for_callback = None
@@ -258,7 +266,9 @@ def main():
                 valid_ds_for_callback = load_dataset(
                     args.valid_name_or_path_for_callback, num_proc=args.workers)
         valid_ds_for_callback = valid_ds_for_callback[args.valid_split_name_for_callback or 'train'].map(
-            AngleDataTokenizer(model.tokenizer, model.max_length, prompt_template=args.prompt_template),
+            AngleDataTokenizer(model.tokenizer, model.max_length,
+                               prompt_template=args.prompt_template,
+                               fix_data=args.fix_data),
             num_proc=args.workers)
 
     argument_kwargs = {}